POS0763 A MULTIMODAL MAGNETIC RESONANCE IMAGING STUDY OF COGNITIVE FUNCTION IN SYSTEMIC LUPUS ERYTHEMATOSUS: A MACHINE LEARNING APPROACH
Authors: S. H. Tay, M. Stephenson, N. A. Allameen, S. Narayanan, B. Lee, A. Mak
Journal: Annals of the Rheumatic Diseases (2022)

Background Systemic lupus erythematosus (SLE) is a multisystem autoimmune disorder that can affect the central nervous system. Cognitive dysfuncion is the most common neuropsyhiatric event in SLE patients, yet it is also one of the hardest to diagnose. Objectives To investigate if multimodal imaging to assess anatomical magnetic resonance imaging (MRI) abnormalities in the brains of SLE patients can predict cognitive function. Methods Subjects underwent voxel-based morphometry (VBM), magnetization transfer imaging (MTI), and dynamic contrast-enhanced (DCE) MRI. Automated Neuropsychological Assessment Metrics (ANAM) was used to assess cognitive function in this cross-sectional study and the primary measure was the total throughput score (TTS). TTS is the total of the throughput scores for each of the 8 ANAM subtests: (i) code substitution learning (CSL); (ii) code substitution immediate (CSI); (iii) code substitution delayed (CSD); (iv) spatial processing (SP); (v) matching to sample (MSP); (vi) running memory continuous performance test (CPT); (vii) mathematical processing (MTH) and (viii) memory search (MS). Olfactory assessment was done using the University of Pennsylvania Smell Identification Test. We used a machine learning-based model (i.e. GLMnet) to predict TTS. Subjects with active SLE disease or above 40 years old were excluded. Results Thirty SLE patients [26 female, 32.0 (26.8-37.0) years] without clinically overt neuropsychiatric manifestations and 10 healthy controls (HCs) [9 females, 27.0 (23.0-31.5) years] were enrolled in this study. Both groups had comparable cognitive and olfactory functions. No significant differences were observed in VBM, MTR, olfactory blub and tract (OBT) volume in SLE patients compared to HCs. We observed increased blood-brain barrier (BBB) permeability parameters (Ktrans and PS) in several regions of SLE patients. DCE-MRI perfusion parameters such as perfusion (F) and vp but not permeability measures were associated with TTS. In particular, F right amygdala correlated with TTS in SLE patients (r = 0.636, FDR p &lt; 0.05) (Table 1). Using GLMnet, we trained a multimodal MRI model comprising of VBM, MTR, DCE-MRI and OBT volume parameters to predict TTS in SLE patients (r = 0.998, p &lt; 0.0005) (Figure 1). Figure 1. Machine learning-based models to predict cognitive function. Table 1. Correlation between ANAM tests with perfusion (F) in SLE patients, ranked in descending order of statistical significance for TTS. Variable TTS CSL CSI CSD SP MSP CPT MTH MS F right amygdala 0.636‡* 0.520‡ 0.337 0.437† 0.559‡ 0.323 0.633‡ 0.412† 0.598‡ F left entorhinal 0.504‡ 0.422† 0.366 0.416† 0.305 0.185 0.530‡ 0.186 0.416† F left amygdale 0.495‡ 0.400† 0.189 0.378† 0.330 0.237 0.491‡ 0.376† 0.449† F choroid 0.469† 0.384† 0.216 0.413† 0.458† 0.202 0.456† 0.340 0.406† plexus F right rostal anterior cingulate 0.453† 0.301 0.118 0.296 0.393† 0.214 0.547‡ 0.420† 0.383† F right entorhinal 0.448† 0.368† 0.232 0.312 0.376† 0.156 0.438† 0.271 0.407† F cerebellum white matter 0.427† 0.358 0.201 0.370† 0.273 0.078 0.449† 0.290 0.297 F left hippocampus 0.427† 0.355 0.134 0.390† 0.356 0.203 0.511‡ 0.336 0.332 F brain stem 0.407† 0.298 0.138 0.275 0.294 0.153 0.478‡ 0.308 0.369† F right insula 0.407† 0.308 0.074 0.300 0.324 0.176 0.437† 0.323 0.347 F left parietal 0.400† 0.263 0.092 0.254 0.294 0.224 0.487‡ 0.274 0.332 F ventricles 0.396† 0.303 0.083 0.321 0.370† 0.192 0.477‡ 0.286 0.361 F right temporal 0.395† 0.280 0.113 0.281 0.288 0.167 0.477‡ 0.322 0.331 F right hippocampus 0.395† 0.307 0.077 0.325 0.356 0.190 0.486‡ 0.357 0.339 F right parietal 0.376† 0.249 0.082 0.274 0.283 0.139 0.460† 0.255 0.311 F right parahippocampal gyrus 0.375† 0.353 0.119 0.302 0.341 0.241 0.353 0.208 0.273 † p &lt; 0.05, ‡ p &lt; 0.01, *FDR p &lt; 0.05 Conclusion These findings suggest that the BBB may be affected early in the course of cognitive dysfunction, even preceding detectable changes in other MRI sequences and machine learning algorithms can be used to predict TTS measures, even in asymptomatic SLE patients. References Nil. Disclosure of Interests Sen Hee Tay: None declared, Mary Stephenson: None declared, Nur Azizah Allameen: None declared, Sriram Narayanan: None declared, Bernett Lee: None declared, Anselm Mak Speakers bureau: JnJ Apr 2019 and GSK Jan 2022, Grant/research support from: GSK - The Supported Studies Programme